Predicting moisture content in a pine logwood pile for energy purposes

Abstract

Determining the moisture content of naturally dried fuel stock without frequent measuring is a problem still unsolved. Modelling moisture content based on automatically captured meteorological data could provide a solution. An accurate model would allow the drying period and the point of chipping to be optimised. For the experimental study, a metal frame supported by load sensors and loaded with 17 tons of logwood was set up next to a meteorological station. A multiple linear regression model was used to link meteorological and load data to provide a formula for determining the moisture content. The pile dried for a period of 14 months (average temperature of 7.3 °C, a humidity of 81%, and 777 mm of rainfall). The overall moisture content dropped from 50.1% to 32.2%. The regression model, which based on daily means and sums of meteorological parameters, provided a mean deviance from the observed curve of –0.51%±0.71% within the period of investigation. Relative humidity was found to be most important parameter in drying. Increased moisture content resulting from rainfall greater than 30 mm per day reverted back to pre-rainfall values within two to three days, if no other rainfall events followed. Covering the pile would have a positive effect on the drying performance. In terms of economic benefit it could be shown that natural drying is beneficial. Overall this study shows that meteorological data used in site specific drying models can adequately predict the moisture content of naturally dried logwood.